AI That Runs in Production, Not Just in a Demo.
Most AI pilots never ship. We build retrieval-augmented generation pipelines and autonomous agents the same way we build any other production system version-controlled, evaluated, and monitored so what works in a demo still works under real traffic, with real data, six months later.
That covers everything from grounding an LLM in your own documents and databases, to agents that plan and execute multi-step work across your existing tools, to the automation layer that connects it all to the rest of your stack.
RAG Built on Your Data
A model that hallucinates answers is worse than no model at all. We build retrieval pipelines that ground every response in your actual documents, tickets, and databases with the chunking, embedding, and evaluation strategy tuned to your content, not a generic default.
- ✓ Document, ticket, and database ingestion pipelines
- ✓ Vector search with pgvector, Pinecone, or Weaviate
- ✓ Retrieval evaluation, so accuracy is measured, not assumed
RAG Pipeline Design
Ingestion, chunking, embeddings, and retrieval tuned to your content, built with LangChain and evaluated against real queries not a generic default.
Agent Orchestration
Multi-step, tool-using agents built with LangGraph with the state management and human-in-the-loop checkpoints that keep autonomy safe.
Workflow Automation
n8n workflows that connect your agents and RAG pipelines to the tools you already run CRMs, inboxes, internal APIs, and more.
Agents That Do the Work, Not Just Chat About It.
A chatbot that answers questions is a demo. An agent that plans a task, calls the right tools, checks its own work, and knows when to hand off to a human is a system you can actually run a business process on. We build that with LangGraph for orchestration and state, LangChain for the model and retrieval layer underneath it, and Claude Code where the task itself is writing or maintaining code.
n8n sits alongside that stack as the automation layer the piece that triggers an agent from an inbound email, posts its output to Slack, or writes the result back to your CRM, without custom glue code for every integration.
Our AI & automation stack
- ✓ LangChain
- ✓ LangGraph
- ✓ Claude Code
- ✓ n8n
- ✓ Vector databases (pgvector, Pinecone)
- ✓ Model evaluation & observability
Autonomy With Guardrails
Every agent we ship has explicit state, logged reasoning steps, and a defined point where it stops and asks a human not an open-ended loop hoping the model gets it right. LangGraph gives us that state machine; we add the evaluation and monitoring on top so you can see exactly what an agent did and why.
Model Integration
Claude, GPT, and open-weight models wired into your product through LangChain, with the prompt and eval work to make outputs consistent.
- › Provider-agnostic integration
- › Prompt versioning & evals
- › Cost and latency tuning
Claude Code for Engineering
We use Claude Code as an agentic pair for our own engineering work, and help teams adopt it safely inside their codebase and CI.
- › Agentic coding workflows
- › Codebase-aware automation
- › Guardrails for CI/CD use
Data Foundation
RAG and agents are only as good as the data underneath them we build the pipelines that keep it current and queryable.
- › Ingestion & sync pipelines
- › Embedding refresh strategy
- › Access control on retrieval
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